Analyzing and Comparing On-Line News Sources via (Two-Layer) Incremental Clustering

Authors Francesco Cambi, Pierluigi Crescenzi, Linda Pagli



PDF
Thumbnail PDF

File

LIPIcs.FUN.2016.9.pdf
  • Filesize: 2.52 MB
  • 14 pages

Document Identifiers

Author Details

Francesco Cambi
Pierluigi Crescenzi
Linda Pagli

Cite AsGet BibTex

Francesco Cambi, Pierluigi Crescenzi, and Linda Pagli. Analyzing and Comparing On-Line News Sources via (Two-Layer) Incremental Clustering. In 8th International Conference on Fun with Algorithms (FUN 2016). Leibniz International Proceedings in Informatics (LIPIcs), Volume 49, pp. 9:1-9:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)
https://doi.org/10.4230/LIPIcs.FUN.2016.9

Abstract

In this paper, we analyse the contents of the web site of two Italian press agencies and of four of the most popular Italian newspapers, in order to answer questions such as what are the most relevant news, what is the average life of news, and how much different are different sites. To this aim, we have developed a web-based application which hourly collects the articles in the main column of the six web sites, implements an incremental clustering algorithm for grouping the articles into news, and finally allows the user to see the answer to the above questions. We have also designed and implemented a two-layer modification of the incremental clustering algorithm and executed some preliminary experimental evaluation of this modification: it turns out that the two-layer clustering is extremely efficient in terms of time performances, and it has quite good performances in terms of precision and recall.
Keywords
  • text mining
  • incremental clustering
  • on-line news

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. J. Azzopardi and C. Staff. Incremental Clustering of News Reports. Algorithms, 5:364-378, 2012. Google Scholar
  2. D. Bhattacharya and S. Ram. Sharing News Articles Using 140 Characters: A Diffusion Analysis on Twitter. In IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pages 966-971, 2012. Google Scholar
  3. Jon Borglund. Event-Centric Clustering of News Articles. Technical report, Department of Information Technology, University of Uppsala, 2013. Google Scholar
  4. T.F. Cox and M.A.A. Cox. Multidimensional Scaling (2nd ed.). Chapman and Hall, 2000. Google Scholar
  5. S. Edunov, C.G. Diuk, I.O. Filiz, S. Bhagat, and M. Burke. Three and a half degrees of separation, 2016. URL: http://research.facebook.com/blog/.
  6. R. Fagin, R. Kumar, and D. Sivakumar. Comparing Top K Lists. In Proceedings of the 14th Annual ACM-SIAM Symposium on Discrete Algorithms, pages 28-36, 2003. Google Scholar
  7. M. Kendall and J. D. Gibbons. Rank Correlation Methods. Edward Arnold, 1990. Google Scholar
  8. J. Leskovec, A. Rajaraman, and J.D. Ullman. Mining of Massive Datasets. Cambridge University Press, 2014. Google Scholar
  9. Vladimir I. Levenshtein. Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady, 10:707-710, 1966. Google Scholar
  10. J.B. Lovins. Development of a Stemming Algorithm. Mechanical Translation and Computational Linguistics, 11:22-31, 1968. Google Scholar
  11. Parse.ly. What is the Lifespan of an Article?, 2015. URL: http://parsely.com.
  12. G. Petkos, S. Papadopoulos, and Y. Kompatsiaris. Two-level Message Clustering for Topic Detection in Twitter. In SNOW 2014 Data Challenge co-located with 23rd International World Wide Web Conference, pages 49-56, 2014. Google Scholar
  13. Wikipedia - News Agency. URL: https://en.wikipedia.org/wiki/News_agency.
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail